Cognitive Profiles Stratified by Education Using Montreal Cognitive Assessment in Parkinson’s Disease Patients with Freezing of Gait
Bibliographic record
Abstract
Purpose: Parkinson’s disease (PD) patients with freezing of gait (FOG) may present with complex and heterogeneous cognitive profiles. Owing to limited access to comprehensive neuropsychological battery in ordinary clinical practice, the Montreal Cognitive Assessment (MoCA) is likely to be easily available cognitive data for comparisons among studies. This study aims to explore the cognitive profiles stratified by education using MoCA in PD patients with FOG. Patients and Methods: PD patients with FOG (FOG+, n = 52) and without FOG (FOG-, n = 71) were included in our study. MoCA items were categorized into five subsections (attention/working memory, executive function, episodic memory, language, and visuospatial function) referring to previously published criteria. Cognitive assessments were compared based on five subsections between groups stratified by three education levels (0– 6 years, 7– 12 years, and > 12 years). The association of cognitive measurements with FOG were analyzed using binary logistic regression models with adjustment for variables. Results: The total scores and subscores of each subsection of MoCA were similar between two groups of each education level. Further detailed analysis showed that a composite measure labeled “Attention/working memory-Composite” (abbreviated to Attention-C), consisting of the scores of four items (target detection task, serial sevens, digit forward and backward, and sentence repetition), were lower significantly in FOG+ group compared with FOG- group in patients with education year ≤ 6 years. The significant association of Attention-C with FOG held true when controlling for disease duration, but not for H-Y stage, MDS-UPDRS III, HAMA, and HAMD. Conclusion: Overall, our findings gave a hint that Attention-C derived from MoCA might be a potential factor associated with FOG in PD patients with lower education level (education year ≤ 6 years), which will need to be validated in future studies. Plain Language Summary: Parkinson’s disease (PD) patients with freezing of gait (FOG) may present with complex and heterogeneous cognitive profiles. Owing to limited access to comprehensive neuropsychological battery in ordinary clinical practice, the Montreal Cognitive Assessment (MoCA) is likely to be easily available cognitive data for comparisons among studies. To the best of our knowledge, our study is the first to investigate the cognitive profiles using MoCA stratified by education in Chinese PD patients with FOG. We proposed a novel composite measure derived from MoCA labeled “Attention/working memory-Composite” (abbreviated to Attention-C), consisting of the scores of four items (target detection task, serial sevens, digit forward and backward, and sentence repetition). We found a significant association of Attention-C with FOG existed in PD patients with education year ≤ 6 years when controlling for disease duration, but disappeared for H-Y stage, MDS-UPDRS III, HAMA, and HAMD. It may be attributed to a large extent to the relatively small size of our study population. Our findings gave a hint that Attention-C derived from MoCA might be a potential factor associated with FOG in PD patients with lower education level (education year ≤ 6 years), which need to be further explored and validated in expanded studies with different international populations. Thus, the present study could provide critical implications for ordinary clinical practice where comprehensive neuropsychological batteries are not easily accessible to clinicians. Keywords: Parkinson’s disease, freezing of gait, Montreal Cognitive Assessment, attention, working memory, executive function
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".